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A human gene makes mice squeak differently — did it contribute to language?
Correction for Hasson et al., Automated determination of transport and depositional environments in sand and sandstones
Daily briefing: CAR-T-cell therapy recipient nears two decades in cancer remission
Correction for Li et al., Recurrent DNA nicks drive massive expansions of (GAA) <sub>n</sub> repeats
Correction for Joshy et al., Accelerated cell-type-specific regulatory evolution of the human brain
‘Unconventional’ nickel superconductor excites physicists
Astragalin relieves inflammatory pain and negative mood in CFA mice by down-regulating mGluR5 signaling pathway
Long-term efficacy of botulinum toxin for treatment of acquired non-accommodative comitant esotropia
Research on the dynamic performance and motion control methods of deep-sea human occupied vehicles
Predicting cell properties with AI from 3D imaging flow cytometer data
Abstract Predicting the properties of tissues or organisms from the genomics data is widely accepted by the medical community. Here we ask a question: can we predict the properties of each individual cell? Single-cell genomics does not work because the RNA sequencing process destroys the cell, not allowing us to verify our predictions. To test the hypothesis, we investigate the approach of using AI to analyze single-cell images obtained from a 3D imaging flow cytometer. We analyze the cell image at day zero and make the AI-assisted cell property prediction. The prediction is then examined later when the cells continue to live and develop. Our preliminary results are promising, showing 88% accuracy in predicting cells that will have a high protein expression level. The technique can have strong ramifications and impact on preventive medicine, drug development, cell therapy, and fundamental biomedical research.
Associations of excessive gestational weight gain with changes in components of maternal reverse cholesterol transport and neonatal outcomes
Daily briefing: The ‘dark side’ of the Asilomar conference
An 8-point scale lung ultrasound scoring network fusing local detail and global features
Assessment of the bidirectional causal association between Helicobacter pylori infection and allergic diseases by mendelian randomization analysis
Surgical outcomes of unilateral medial rectus recession for partially accommodative esotropia
Behavioral and multiomics analysis of 3D clinostat simulated microgravity effect in mice focusing on the central nervous system
Abstract A study was conducted to evaluate the three-dimensional clinostat simulated microgravity effect on mouse models, focusing on the central nervous system. Eighteen mice were divided into three groups: control, survival box, and clinostat + survival box. Behavioral tests, femur micro-CT, brain transcriptomics, serum metabolomics, and fecal microbiomics were performed. Results showed decreased activity, altered gait, enhanced fear memory, bone loss, immune/endocrine changes in brain transcriptome, and altered metabolic pathways in serum and gut microbiota in clinostat-treated mice. The model closely mimics spaceflight-induced transcriptome changes, suggesting its value in studying microgravity-related neurological alterations and highlighting the need for attention to emotional changes in space.
Experimental study on anti-slip performance of galvanized cable-clamped joint at elevated temperature
A study of fracture mechanics for compact tensile specimen of Al6061-SiC metal matrix composite
Abstract The main contribution of the present work is the display of the impact of the addition of SiC into the aluminum alloy Al6061. For this reason, the Mode I stress intensity factor KI and T-stress for compact tension CT specimen are evaluated using 3D finite element analysis (FEA). The material used here in the compact tension CT specimen is Al6061-SiC metal matrix composites reinforced with various volume fractions of 4%, 6%, 10%, 12%, and 14% of SiC particles. Three different crack lengths (a/H) ratios of 0.35, 0.43, and 0.5 are considered through the analysis. Only half of the model of the cracked compact tension CT specimen with a subjected load of a magnitude P = 603 N is analyzed, and KI, T11-stress, and T33-stress are computed. From the FEA results, it is observed that the KI, T11-stress, and T33-stress are mainly influenced by the volume fractions of reinforced SiC particles. A more significant decrease in the values of KI, T11-stress, and T33-stress is found in the Al6061-14vol.%SiC composite CT specimen. Where FEA results of KI for the Al6061-14vol.%SiC composite CT specimen exhibited reduction percentages of 5.4%, 5.6%, and 5.7%, respectively, for (a/H) = 0.35, 0.43, and 0.5, as compared to those of Al6061. FEA values of T11-stress for the Al6061-14vol.%SiC composite CT specimen reduced by 5.5%, 5.6%, and 5.7%, respectively, for (a/H) = 0.35, 0.43, and 0.5, respectively, over those of Al6061. Also, the decrement percentages of FEA results of T33-stress for the Al6061-14vol.%SiC composite CT specimen over those of Al6061 were found to be 17.1%, 16.6%, and 16.5%, respectively, for (a/H) = 0.35, 0.43, and 0.5, respectively. Overall, fracture mechanics properties are improved by the addition of SiC particulates into the Al6061 alloy.